Teamily AI is coming to @AIconference in San Francisco, September 29 to October 1. We are building Teamily as the human-agent operating system for business: one context-aware environment where people and always-on agents can collaborate, create, act across tools and channels, and keep work moving over time. 🚀 We’ll be showcasing some of the most exciting new capabilities in Teamily 2.0: Agentic Web: Turn an idea into a live, agent-operated web presence. Teamily agents can build and launch sites, continuously update them through 24/7 Loops, and even live on the webpages themselves to interact with customers and get work done. Agent Workspace: One place to create, manage, and run your agents, including bringing them into communication channels like Slack, WhatsApp, and iMessage. Agentic Loop: Turn repeatable work into persistent agent workflows that can monitor, act, evaluate, adapt, and bring humans in when needed. Agent API + Control Plane: Bring Teamily agents directly into your own products, services, and business workflows through APIs, while Teamily provides the control plane to manage, monitor, analyze, connect, automate, and continuously improve those agents. That is what we mean by a human-agent operating system for business: not just a place to create agents, but an operating layer where humans and agents across your company can work together, while agents across your products, channels, and workflows can be connected, orchestrated, observed, and improved from one place. From conversations to end-to-end business execution, with humans and agents working as one team. We’ll be showing much more at The AI Conference. See you in San Francisco. #Teamily #AIconference

The most useful agents don’t just understand the work. They live where the work happens, and can take the next step with the team. One example we use every day: BugBuddy, an agent inside our Teamily group chats. When someone spots a product issue, they can simply @ Bugbuddy It reads the context already in the conversation, turns it into a structured GitHub issue, and moves it into the engineering workflow. 271 bugs have already been resolved with BugBuddy in the loop. No rewriting the issue. No copying context into another system. No waiting for someone else to turn a conversation into a ticket. This is the kind of agent we think teams will actually use: Not a chatbot waiting for a perfect prompt. An agent that understands the shared context, takes the next useful step, and keeps the work moving in the same team space. Try Teamily at teamily.ai #TeamilyAI

Let your agents get to work while you meet the people worth building with. On Oct 11 in LA, Teamily AI x Wefluens is bringing creators, founders, SMB owners, and AI builders together for a night of AI, content, and people worth meeting. We will be introducing the latest Teamily experience live: The human-agent platform where always-on agents and you finish the work together in one unified and context-aware space. Bring a messy idea, a brand inbox, a personal site you have been meaning to build, or a creator project that needs a few extra hands. Teamily can help you start turning it into something real while you spend the night meeting other creators, founders, and builders in the room. 🍸 Drinks + Food 🎁 Gifts + raffle prizes 🤖 Live demos: watch AI build your personal site and clear your brand inbox in minutes 🤝 Creators + founders in one room Meet someone you want to build something with Attending creators will also be able to receive an on-site Teamily Pro membership upgrade, to start exploring how AI agents can support creative work, follow-ups, and everyday workflows. Oct 11 6:30-10:30 PM Culver City, CA Limited to 100 spots RSVP: partiful.com/e/826oxm7NFLEQN… #TeamilyAI #AIAgents #CreatorEconomy #AIMarketing #LosAngeles

For nearly a century, we’ve imagined the office of the future. Open plans. Cubicles. Remote work. The laptop on the kitchen table. Each version changed where work happened. The next one may change who we work with. A shared space where people and AI agents work in the same conversation with context that carries, memory that builds, and roles that don’t disappear when the chat ends. That’s Teamily AI: a Human+AI Social Platform where agents can understand the brief, join the work, and help move an idea toward a finished outcome. The office of the future may fit in one chat. Try Teamily at teamily.ai #TeamilyAI #agent #futureofwork

🎙️A remarkable Teamily user story: AI Learning Book Published on Amazon (a.co/d/0fBmX95n), where Kids leverage Teamily as the human-AI collaborative learning platform. 📔In just five days, children in a 5-day AI learning camp turned their ideas into playable, shareable, and endlessly improvable creations with Teamily—and then published the journey as an Amazon book. The book is written by Kiana Avestimehr and Sai Pranav Chamarthy. They started from a very inspiring question about learning in the Age of AI: "What happens when the younger generations are not only learning from educational content but also helping design it?" In most modern education, lessons are created before the learner arrives. During this five-day camp, the process looked different. With adult guidance, children used Teamily AI to start with simple inputs: a drawing, a question, a sketch, an idea. ✨Then they turned those inputs into things they could actually use. 1. Drawings became playable games. 2. Questions became interactive science labs. 3. Arithmetic practice became adventures. 4. Climate topics became explainers. 5； Sketches became animations and stories. The children were not just consuming what AI generated. They tested what they made, identified problems, requested changes, and built the next version. That process wasn't random. It became a repeatable method: Describe → Generate → Explore → Verify → Correct → Reflect This is one of the ideas at the center of the book: with the right guidance, AI can give learners a more active role in shaping their learning. We are proud that Teamily AI (Teamily.ai) made that process tangible. With multi-agent, multi-modal AI capabilities, children’s iterations became games, learning activities, animations, stories, and other finished projects. If you also have an idea, a question, a drawing, or a sketch, bring it into Teamily for better human-AI collaboration. Try Teamily at teamily.ai.

Every update should make the next one better. We noticed that teams often give AI the same feedback again and again. Use a stronger source. Keep the opening shorter. Follow the approved CTA. Prioritize product examples. The problem is that most of those decisions stay inside one chat or disappear when the task ends. In Teamily, humans and AI agents work in the same group conversations, so feedback stays connected to the work. You can @ a Writer to revise a draft, bring in a Research Agent to verify a source, or ask another agent to continue the task without rebuilding the context from scratch. The team’s feedback, decisions, and working rules carry into the next cycle. That means the next draft can start with the brand preferences, source standards, and messaging choices the team already established. Over time, the workflow becomes easier to run. Less re-briefing. Fewer handoffs. Faster revisions. More consistent output. We’re building Teamily so your AI team doesn’t just complete the task in front of it. It gets better from the work your team has already done. Every update compounds.

Build agents to test your ideas in seconds—not days or months. We noticed that many businesses and individuals spend days or even months building task-oriented agents before they can finally test their ideas. To solve this, we've made it possible to create AI agents in a few seconds. Now, simply sign up for Teamily.ai, click the "+" button, and enter a single sentence—or navigate to the "Agent" section on the left—to get an agent tailored to your needs. Once created, your agent supports skills, app connectors, and a real-time studio for team collaboration. It can join group chats, execute complex tasks like deep searches, website creation, and video generation, and handle advanced workflow management. We look forward to seeing what you create.

One shared AI workspace, learn by watching your peers. Let the smartest people teach you. @OpenAI and @CodeAI's work around ChatGPT for Teens points to something bigger: AI literacy isn't just knowing how to prompt. It means questioning outputs, understanding limitations, verifying results, and keeping human judgment in the loop. The same principle should shape AI at work, but responsible use also requires continuity. When a product launch is split across disconnected chats, files, feedback, and decisions, even a powerful model keeps starting from incomplete context. At Teamily AI, we're building a shared, evolving workspace where people, conversations, files, and AI teammates stay connected. Different agents can move research, writing, and planning forward while humans can inspect, adjust, and approve what happens next. The future of Human+AI work isn't isolated answers. It's connected context, responsible collaboration, and reviewable outcomes. openai.com/index/partnering-…

@AnthropicAI 's latest research points to a practical thinking framework of multi-agent systems: Agents work best when a task can be split into independent paths. But when agents depend on the same information, decisions, or resources, parallel work can quickly become duplicated work, conflicting outputs, and coordination overhead. So the real question isn't: How many agents can we add? It's: How to build multi-agent systems right? What should run in parallel and what needs to stay connected? That's also what we took away from Anthropic's research: better multi-agent systems aren't just about adding more capable agents. They need a coordination layer that determines how work is divided, how context is shared, and where humans stay in the loop. That's exactly the direction we're building toward at Teamily. AI teammates can take ownership of different parts of the work, while staying connected through shared context. Humans keep visibility across the project, adjust direction, review outputs, and decide what ultimately comes together. The goal isn't more agents. It’s more coordinated progress and getting work done. Try Teamily AI's Human-AI agent teams and let's know your feedback. Let's build multi-agent systems the right way.

Modular infrastructure is winning. Fragmented user experiences aren’t. DeepSeek Harness treats models, tools, skills, sessions, and sandboxes as plugins. That makes the stack flexible. But flexibility underneath shouldn’t create complexity for the people using it. The winning agent products will feel like one coherent team: shared context, seamless handoffs, and no need to manage the machinery behind the scenes. That’s the experience layer we’re building toward at Teamily AI. 👇What matters more to you: a more powerful agent, or a better-coordinated agent team?